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We use a subset of the ChEMBL15 dataset that contains 2,763 associations between 544 drugs and 467 target proteins to evaluate our method, and we extracted datasets of bioactivity ≤1 and ≤10 μM activity cutoff.
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The number of sequences in each of the extracted datasets is included in Table 1.
Some dozen kinds of extracted datasets are available on application.
Since then, dozens of extracted datasets have been available to researchers.
For each RSCU combination, we extracted balanced datasets by randomly sampling an equal number of genes from each tissue-specific list.
The accuracy of how well the extracted dataset relates to the reference dataset [62, 64] is examined.
The extracted dataset was submitted to the same filtering as were the GBS data, notably to remove markers that became monomorphic within each panel.
Since we considered protein functional information in this study, we extracted a dataset containing only human phosphorylation sites containing 11038 entries.
We extracted a dataset that would provide insight into whether the elements of a family history of breast cancer identified in the literature review were recorded.
We extracted a dataset that included Hb, mean corpuscular volume (MCV) and common potential causes of anaemia so that we could differentiate between renal and other causes of anaemia.
To this end, we extracted from the datasets in Table 1 all those loci that are expressed in multiple experiments.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com